Author
Listed:
- Jamie M. Caldwell
(Stanford University)
- A. Desiree LaBeaud
(Stanford University)
- Eric F. Lambin
(Stanford University
Université Catholique de Louvain)
- Anna M. Stewart-Ibarra
(SUNY Upstate Medical University
InterAmerican Institute for Global Change Research (IAI))
- Bryson A. Ndenga
(Centre for Global Health Research, Kenya Medical Research Institute)
- Francis M. Mutuku
(Technical university of Mombasa)
- Amy R. Krystosik
(Stanford University)
- Efraín Beltrán Ayala
(Technical University of Machala)
- Assaf Anyamba
(Universities Space Research Association and NASA Goddard Space Flight Center)
- Mercy J. Borbor-Cordova
(Facultad de Ingeniería Marítima y Ciencias del Mar, Escuela Superior Politécnica del Litoral, ESPOL)
- Richard Damoah
(Morgan State University and NASA Goddard Space Flight Center)
- Elysse N. Grossi-Soyster
(Stanford University)
- Froilán Heras Heras
(Center for Research SUNY-Upstate-Teófilo Dávila Hospital)
- Harun N. Ngugi
(Chuka University
School of Biological Sciences University of Nairobi)
- Sadie J. Ryan
(University of Florida
University of Florida
University of KwaZulu)
- Melisa M. Shah
(Stanford University)
- Rachel Sippy
(Center for Research SUNY-Upstate-Teófilo Dávila Hospital
SUNY-Upstate Medical University
University of Florida)
- Erin A. Mordecai
(Stanford University)
Abstract
Climate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases, such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (28–85% for vectors, 44–88% for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections.
Suggested Citation
Jamie M. Caldwell & A. Desiree LaBeaud & Eric F. Lambin & Anna M. Stewart-Ibarra & Bryson A. Ndenga & Francis M. Mutuku & Amy R. Krystosik & Efraín Beltrán Ayala & Assaf Anyamba & Mercy J. Borbor-Cord, 2021.
"Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents,"
Nature Communications, Nature, vol. 12(1), pages 1-13, December.
Handle:
RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21496-7
DOI: 10.1038/s41467-021-21496-7
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Cited by:
- Haobo Ni & Xiaoyan Cai & Jiarong Ren & Tingting Dai & Jiayi Zhou & Jiumin Lin & Li Wang & Lingxi Wang & Sen Pei & Yunchong Yao & Ting Xu & Lina Xiao & Qiyong Liu & Xiaobo Liu & Pi Guo, 2024.
"Epidemiological characteristics and transmission dynamics of dengue fever in China,"
Nature Communications, Nature, vol. 15(1), pages 1-14, December.
- Rory Gibb & Felipe J. Colón-González & Phan Trong Lan & Phan Thi Huong & Vu Sinh Nam & Vu Trong Duoc & Do Thai Hung & Nguyễn Thanh Dong & Vien Chinh Chien & Ly Thi Thuy Trang & Do Kien Quoc & Tran Min, 2023.
"Interactions between climate change, urban infrastructure and mobility are driving dengue emergence in Vietnam,"
Nature Communications, Nature, vol. 14(1), pages 1-15, December.
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